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Direct linkNeil C. C. Brown; Pierre Weill-Tessier; Juho Leinonen; Paul Denny; Michael Kölling – ACM Transactions on Computing Education, 2025
Motivation: Students learning to program often reach states where they are stuck and can make no forward progress--but this may be outside the classroom where no instructor is available to help. In this situation, an automatically generated next-step hint can help them make forward progress and support their learning. It is important to know what…
Descriptors: Artificial Intelligence, Programming, Novices, Technology Uses in Education
Lauren E. Margulieux; Yin-Chan Liao; Erin Anderson; Miranda C. Parker; Brendan D. Calandra – ACM Transactions on Computing Education, 2024
Integrated computing curricula combine learning objectives in computing with those in another discipline, like literacy, math, or science, to give all students experience with computing, typically before they must decide whether to take standalone CS courses. One goal of integrated computing curricula is to provide an accessible path to an…
Descriptors: Technology Uses in Education, Technology Integration, Computer Uses in Education, Computer Science
Pavlos Toukiloglou; Stelios Xinogalos – Education and Information Technologies, 2024
Hour of Code is a widely recognized global event that aims to introduce programming to novice users and integrate computer science into education. This paper presents an analysis of the effectiveness of the support system and user interface of Minecraft Adventurer, a serious game designed for the Hour of Code global event. Although previous…
Descriptors: Novices, Programming, Coding, Computer Science Education
Download full textAilsa Zayyan Salsabila; R. Yugo Kartono Isal; Harry B. Santoso – Journal of Educators Online, 2025
This study aims to provide recommendations for interaction design to enhance students' task interpretation, as one of the crucial aspects of Self-Regulated Learning (SRL) in online learning environments. Utilizing the Engineering Design Metacognitive Questionnaire (EDMQ), open-ended questions, and in-depth interviews, this study examines the…
Descriptors: Learning Processes, Electronic Learning, College Students, Computer Science Education
Haley A. Delcher; Enas S. Alsatari; Adeyeye I. Haastrup; Sayema Naaz; Lydia A. Hayes-Guastella; Autumn M. McDaniel; Olivia G. Clark; Devin M. Katerski; Francois O. Prinsloo; Olivia R. Roberts; Meredith A. Shaddix; Bridgette N. Sullivan; Isabella M. Swan; Emily M. Hartsell; Jeffrey D. DeMeis; Sunita S. Paudel; Glen M. Borchert – Biochemistry and Molecular Biology Education, 2025
Today, due to the size of many genomes and the increasingly large sizes of sequencing files, independently analyzing sequencing data is largely impossible for a biologist with little to no programming expertise. As such, biologists are typically faced with the dilemma of either having to spend a significant amount of time and effort to learn how…
Descriptors: Artificial Intelligence, Technology Uses in Education, Training, Teaching Methods
Hüseyin Gokal; Cem Ufuk Baytar – Turkish Online Journal of Educational Technology - TOJET, 2025
This study aims to examine university students' intentions to use artificial intelligence (AI) applications in their educational processes within the context of job characteristics (JC), technology characteristics (TC), task-technology fit (TTF), and self-efficacy (SE). The research was conducted with 965 students enrolled in Information…
Descriptors: College Students, Intention, Technology Uses in Education, Artificial Intelligence
Xiaojing Duan; Bo Pei; G. Alex Ambrose; Arnon Hershkovitz; Ying Cheng; Chaoli Wang – Education and Information Technologies, 2024
Providing educators with understandable, actionable, and trustworthy insights drawn from large-scope heterogeneous learning data is of paramount importance in achieving the full potential of artificial intelligence (AI) in educational settings. Explainable AI (XAI)--contrary to the traditional "black-box" approach--helps fulfilling this…
Descriptors: Academic Achievement, Artificial Intelligence, Prediction, Models
Srecko Stamenkovic; Nenad Jovanovic – IEEE Transactions on Learning Technologies, 2024
Although we are witnessing the accelerated development of computer science, and the opening of new fields of study, compiler construction is still a very important field that is taught at most world universities. Because of a large number of algorithms and complex theoretical constructions, these topics represent a difficult and complex domain for…
Descriptors: Computer Science, Computer Software, Educational Technology, Computer Simulation
A Comparison of Generative AI Solutions and Textbook Solutions in an Introductory Programming Course
Ernst Bekkering; Patrick Harrington – Information Systems Education Journal, 2025
Generative AI has recently gained the ability to generate computer code. This development is bound to affect how computer programming is taught in higher education. We used past programming assignments and solutions for textbook exercises in our introductory programming class to analyze how accurately one of the leading models, ChatGPT, generates…
Descriptors: Higher Education, Artificial Intelligence, Programming, Textbook Evaluation
Kamil Çelik; Ahmet Ayaz – Educational Technology Research and Development, 2025
Technological advancements in recent years have accelerated the development of information and communication technologies, introducing numerous innovations. One prominent innovation is the concept of the metaverse, which has gained significant popularity and is increasingly influencing various sectors, including the economy, art, entertainment,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Intention, Computer Science Education
Jihae Suh; Kyuhan Lee; Jaehwan Lee – Education and Information Technologies, 2025
Artificial Intelligence (AI) has rapidly emerged as a powerful tool with the potential to enhance learning environments. However, effective use of new technologies in education requires a good understanding of the technology and good design for its use. Generative AI such as ChatGPT requires particularly well-designed instructions due to its ease…
Descriptors: Programming, Computer Science Education, Artificial Intelligence, Technology Uses in Education
Senad Orhani; Kyvete Shatri – Educational Process: International Journal, 2025
Background/purpose: In the modernized society with advanced technology, as education becomes increasingly dependent on information technology, there is a need to prepare the next generation of students with a wide variety of skills and abilities necessary for future scientific careers. Modern educational challenges include the task of engaging…
Descriptors: Computer Games, Technology Uses in Education, Game Based Learning, Student Motivation
Jiangyue Liu; Siran Li – Journal of Educational Computing Research, 2024
Pair Programming is considered an effective approach to programming education, but the synchronous collaboration of two programmers involves complex coordination, making this method difficult to be widely adopted in educational settings. Artificial Intelligence (AI) code-generation tools have outstanding capabilities in program generation and…
Descriptors: Artificial Intelligence, Programming, Technology Uses in Education, Coding
Fadoua Balabdaoui; Nora Dittmann-Domenichini; Henry Grosse; Claudia Schlienger; Gerd Kortemeyer – Discover Education, 2024
We report the results of a 4800-respondent survey among students at a technical university regarding their usage of artificial intelligence tools, as well as their expectations and attitudes about these tools. We find that many students have come to differentiated and thoughtful views and decisions regarding the use of artificial intelligence. The…
Descriptors: Foreign Countries, College Students, Artificial Intelligence, Student Attitudes
Leveraging Large Language Models to Generate Course-Specific Semantically Annotated Learning Objects
Dominic Lohr; Marc Berges; Abhishek Chugh; Michael Kohlhase; Dennis Müller – Journal of Computer Assisted Learning, 2025
Background: Over the past few decades, the process and methodology of automatic question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content. Objectives: This paper explores the potential of large language models…
Descriptors: Resource Units, Semantics, Automation, Questioning Techniques
